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003			OSt
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008			250115s2024 ua a|||| |||| 000 0 eng d
040			_aEG-GICUC _beng _cEG-GICUC _dEG-GICUC _erda
041	0		_aeng _beng _bara
049			_aDeposit
082	0	4	_a631.587
092			_a631.587 _221
097			_aM.Sc
099			_aCai01.07.02.M.Sc.2024.Kh.P.
100	0		_aKhadiga Tawfiek Elhussiny Badr, _epreparation.
245	1	0	_aPrediction Of Water Distribution Uniformity Of Sprinkler Irrigation System Based On Machine Learning Algorithms / _c By Khadiga Tawfiek Elhussiny Badr; Supervision Committee Dr. Ahmed Mahrous Hassan, Dr. Ali Mokhtar Mohammed, Dr. Ahmed Reda Abo Habsa, Dr. Mohamed Hanafy Hassan
246	1	5	_a / التنبؤ بانتظامية توزيع المياه لنظام الري بالرش اعتمادا علي خوارزميات التعلم الآلي
264		0	_c2024.
300			_a103 pages : _billustrations ; _c25 cm. + _eCD.
336			_atext _2rda content
337			_aUnmediated _2rdamedia
338			_avolume _2rdacarrier
502			_aThesis (M.Sc.) -Cairo University, 2024.
504			_aBibliography: pages 78-96.
520			_aThe water shortage is one of the main challenges for future water policy. The coefficients of uniformity (Christiansen's uniformity coefficient (CU) and distribution uniformity (DU)) are an important parameter for designing irrigation systems, and these are accurate indicator for water loss. In this study, three machine learning algorithms (RF: Random Forest, XGB: Extreme Gradient Boosting and XGB-RF: Random Forest-Extreme Gradient Boosting), after training the different algorithms and testing it, the best result was as following: using XGB-RF to predict CU and DU with the first scenario. Were developed to predict the water distribution uniformity based on operating pressure, heights of sprinkler, nozzle diameter (discharge), wind speed, relative humidity, maximum and minimum temperature for three different impact sprinklers (KA-4, FOX and 2520) for square and triangular system layout. The main findings were; the highest CU values for (2520 sprinkler) under 200 kPa, 0.5m height, Nozzle 2.5mm was 86.7% in the square system and the discharge was 0.855 m3/h, Meanwhile, in the triangular system, it was 87.3% under the same pressure and discharge but at 1m height. Through the simulation work, the highest values of coefficient of determination (R2) were 0.796, 0.825 and 0.929 in RF, XGB and XGB-RF respectively in the first scenario for CU. Moreover, for the DU, the highest values of R2 were 0.701, 0.479 and 0.826 in RF, XGB and XGB-RF respectively in the first scenario. The obtained results revealed that the machine learning models is promising and can be as a rapid tool for decision-makers to manage the water scarcity.
520			_aفي هذه الرسالة، تم تطوير ثلاث خوارزميات للتعلم الآلي (, XGB ,RF و (XGB - RF للتنبؤ بتوحيد توزيع المياه بناءً على ضغط التشغيل وارتفاعات الرشاش وقطر الفوهة (التصرف) وسرعة الرياح والرطوبة النسبية ودرجة الحرارة العظمي والصغري لثلاث رشاشات (FOX ,KA-4 و (2520 لتخطيط النظام المربع والمثلث. وكانت النتائج الرئيسية هي أعلى قيمة CU كانت 87.3٪ في النظام المثلث للرشاش 2520 تحت ضغط تشغيل 200 كيلو باسكال، وارتفاع 1 متر وتصرف 0,855 متر مكعب/ساعة (قطر الفوهة 2,5 ملم). من خلال أعمال المحاكاة كانت أعلي قيم معامل التحديد (R2) هي 0.796 و 0.825 و 0.929 في RF ، XGB و XGB-RF على الترتيب في السيناريو الأول لـ CU.
530			_aIssued also as CD
546			_aText in English and abstract in Arabic & English.
650		7	_aIrrigation _2qrmak
653		0	_aWater distribution uniformity _asprinkler irrigation _awater shortage _amachine learning algorithms
700	0		_aAhmed Mahrous Hassan _ethesis advisor.
700	0		_aAli Mokhtar Mohammed _ethesis advisor.
700	0		_aAhmed Reda Abou-Habsa _ethesis advisor.
700	0		_aMohamed Hanafy Hassan (deceased) _ethesis advisor.
900			_b01-01-2024 _cAhmed Mahrous Hassan _cAli Mokhtar Mohammed _cAhmed Reda Abou-Habsa _cMohamed Hanafy Hassan (deceased) _UCairo University _FFaculty of Agriculture _DDepartment of Agricultural Engineering
905			_aEman El gebaly _eHuda
942			_2ddc _cTH _e21 _n0
999			_c170218